Research on positioning and mapping algorithm of sliding window optimization for substation monitoring robot

Abstract

This paper proposed a positioning and mapping algorithm of sliding window optimization for substation monitoring robots to solve the problems of low precision location and poor robustness of the existing laser odometer in power inspection outdoor scene mapping. A tightly coupled simultaneous localization and mapping (SLAM) algorithm was proposed based on 16-wire LiDAR and inertial measurement unit (IMU). Firstly, the paper estimated the IMU and corrected the motion distortion of the laser point cloud by linear interpolation. Secondly, scene features were extracted by curvature and classified according to different feature properties. The local map was constructed in the sliding window using the inter-frame matching module. Finally, the joint optimization function was built using the distance and IMU data obtained by matching the frame with the local map. The paper used the KITTI and self-recorded datasets to conduct the experiments. The results show that the improved method’s accuracy outperforms the lightweight and ground-optimized LiDAR odometry and Mapping (Lego-LOAM) and LiDAR inertial odometry and mapping (LIO-Mapping) and draws a broad application prospect in power inspection field.